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At least 325 records · Page 18

CO 2 Transport Infrastructure Outlook in the United States

Carbon capture and storage (CCS) represents one of the most important methods to mitigate anthropogenic carbon emissions at a large scale, playing a key role in meeting climate change targets (Bui et al., 2018) and for net-zero CO 2 by 2050 scenarios in the United States (Browning et al., 2023). This technology involves capturing CO 2 emissions from industrial processes, transporting them via pipelines, trucks, rails, or ships, and ultimately storing them in underground geological sites, such as saline aquifers or depleted oil reservoirs. Thus, to encourage carbon reduction initiatives, the U.S. Congress enacted the Bipartisan Budget Act in 2018, reforming the 45Q tax credit to benefit operators storing CO 2 in geologic formations (Jones and Sherlock, 2021). Additionally, the 2022 Inflation Reduction Act further expanded these incentives, providing additional support for CCS initiatives (Hackett and Kuehn, 2023). Although numerous studies describe the importance of optimal CO 2 transportation to support the decision-making of CCS projects aligned with the objective of net-zero emissions by 2050 (Abramson and Christensen, 2021; Chen and Pawar, 2023; Greig and Pascale, 2021), further efforts are required to optimize the transport infrastructure for national-scale CCS deployment. Therefore, in this study, we examine three nationwide scenarios with the SimCCS 3.0 tool (Ma et al., 2022, 2023, 2024) along with a novel geospatial splitting approach developed by Velasco-Lozano et al. (Velasco- Lozano et al., 2024a, 2024b). We present optimized pipeline networks that meet the dynamic evolution of annual capture amounts, describing the required total pipeline lengths at each stage as a function of the pipeline diameters. Thus, the cases presented demonstrate the feasibility of CO 2 pipeline infrastructure for large-scale CCS projects.

54 ENVIRONMENTAL SCIENCES↗

ACES-GNN: can graph neural network learn to explain activity cliffs?

Graph Neural Networks (GNNs) have revolutionized molecular property prediction by leveraging graph-based representations, yet their opaque decision-making processes hinder broader adoption in drug discovery. This study introduces the Activity-Cliff-Explanation-Supervised GNN (ACES-GNN) framework, designed to simultaneously improve predictive accuracy and interpretability by integrating explanation supervision for activity cliffs (ACs) into GNN training. ACs, defined by structurally similar molecules with significant potency differences, pose challenges for traditional models due to their reliance on shared structural features. By aligning model attributions with chemist-friendly interpretations, the ACES-GNN framework bridges the gap between prediction and explanation. Validated across 30 pharmacological targets, ACES-GNN consistently enhances both predictive accuracy and attribution quality for ACs compared to unsupervised GNNs. Our results demonstrate a positive correlation between improved predictions and accurate explanations, offering a robust and adaptable framework to better understand and interpret ACs. This work underscores the potential of explanation-guided learning to advance interpretable artificial intelligence in molecular modeling and drug discovery.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

INTERSTELLAR ENVIRONMENTS AND DUST PROPERTIES TOWARD CYGNUS OB2 NO. 12: A REASSESSMENT

The B-type hypergiant Cygnus OB2 no. 12 is a popular target for studies of interstellar phenomena at visible-infrared wavelengths because of its exceptional brightness for a star dimmed by some 10 mag of visual extinction. A lack of detectable ice absorption has led investigators to regard the line of sight as a standard for studies of the “diffuse” interstellar medium (ISM), an assumption challenged both by observations of molecular gas toward the star and by uncertainties concerning the degree to which such a luminous object may affect its local environment. This paper presents a reassessment of the nature of the material responsible for extinction toward Cyg OB2 no. 12. The excess relative to other cluster members appears to occur in translucent clumps within an extensive network of clouds in the region. Attenuation of the ambient radiation field is sufficient in the cores of the clumps to support the presence of gas-phase molecules, but not to sustain detectable ice formation. In general, the optical properties of dust in the clumps are closely similar to those observed in typical diffuse interstellar material, with the notable exception of an unusually low value for the wavelength of maximum polarization. The implied enhancement of polarization by small grains is attributed to increased alignment efficiency in an enhanced magnetic field. This caveat apart, the results of the current paper provide reassurance that Cyg OB2 no. 12 is, indeed, an appropriate choice for studies that target diffuse and translucent phases of the ISM.

79 ASTRONOMY AND ASTROPHYSICS↗

Differential CLE peptide perception by plant receptors implicated from structural and functional analyses of TDIF-TDR interactions

Tracheary Element Differentiation Inhibitory Factor (TDIF) belongs to the family of post-translationally modified CLE (CLAVATA3/embryo surrounding region (ESR)-related) peptide hormones that control root growth and define the delicate balance between stem cell proliferation and differentiation in SAM (shoot apical meristem) or RAM (root apical meristem). In Arabidopsis, Tracheary Element Differentiation Inhibitory Factor Receptor (TDR) and its ligand TDIF signaling pathway is involved in the regulation of procambial cell proliferation and inhibiting its differentiation into xylem cells. Here we present the crystal structures of the extracellular domains (ECD) of TDR alone and in complex with its ligand TDIF resolved at 2.65 Åand 2.75 Å respectively. These structures provide insights about the ligand perception and specific interactions between the CLE peptides and their cognate receptors. Our in vitro biochemical studies indicate that the interactions between the ligands and the receptors at the C-terminal anchoring site provide conserved binding. While the binding interactions occurring at the N-terminal anchoring site dictate differential binding specificities between different ligands and receptors. Our studies will open different unknown avenues of TDR-TDIF signaling pathways that will enhance our knowledge in this field highlighting the receptor ligand interaction, receptor activation, signaling network, modes of action and will serve as a structure function relationship model between the ligand and the receptor for various similar leucine-rich repeat receptor-like kinases (LRR-RLKs).

59 BASIC BIOLOGICAL SCIENCES↗

Effect of Biomass Water Dynamics in Cosmic-Ray Neutron Sensor Observations: A Long-Term Analysis of Maize–Soybean Rotation in Nebraska

Precise soil water content (SWC) measurement is crucial for effective water resource management. This study utilizes the Cosmic-Ray Neutron Sensor (CRNS) for area-averaged SWC measurements, emphasizing the need to consider all hydrogen sources, including time-variable plant biomass and water content. Near Mead, Nebraska, three field sites (CSP1, CSP2, and CSP3) growing a maize–soybean rotation were monitored for 5 (CSP1 and CSP2) and 13 (CSP3) years. Data collection included destructive biomass water equivalent (BWE) biweekly sampling, epithermal neutron counts, atmospheric meteorological variables, and point-scale SWC from a sparse time domain reflectometry (TDR) network (four locations and five depths). In 2023, dense gravimetric SWC surveys were collected eight (CSP1 and CSP2) and nine (CSP3) times over the growing season (April to October). The N0 parameter exhibited a linear relationship with BWE, suggesting that a straightforward vegetation correction factor may be suitable (fb). Results from the 2023 gravimetric surveys and long-term TDR data indicated a neutron count rate reduction of about 1% for every 1 kg m−2 (or mm of water) increase in BWE. This reduction factor aligns with existing shorter-term row crop studies but nearly doubles the value previously reported for forests. This long-term study contributes insights into the vegetation correction factor for CRNS, helping resolve a long-standing issue within the CRNS community.

Chemistry↗

Imaging and quantitative data acquisition of biological cell walls with Atomic Force Microscopy and Scanning Acoustic Microscopy

This chapter demonstrates the feasibility of Atomic Force Microscopy (AFM) and High Frequency Scanning Acoustic Microscopy (HF-SAM) as tools to characterize biological tissues. Both the AFM and the SAM have shown to provide imaging (with different resolution) and quantitative elasticity measuring abilities. Plant cell walls with minimal disturbance and under conditions of their native state have been examined with these two kinds of microscopy. After descriptions of both the SAM and AFM, their special features and the typical sample preparation is discussed. The sample preparation is focused here on epidermal peels of onion scales and celery epidermis cells which were sectioned for the AFM to visualize the inner surface (closest to the plasma membrane) of the outer epidermal wall. The nm-wide cellulose microfibrils orientation and multilayer structure were clearly observed. The microfibril orientation and alignment tend to be more organized in older scales compared with younger scales. The onion epidermis cell wall was also used as a test analog to study cell wall elasticity by the AFM nanoindentation and the SAM V(z) feature. The novelty in this work was to demonstrate the capability of these two techniques to analyze isolated, single layered plant cell walls in their natural state. AFM nanoindentation was also used to probe the effects of Ethylenediaminetetraacetic acid (EDTA), and calcium ion treatment to modify pectin networks in cell walls. The results suggest a significant modulus increase in the calcium ion treatment and a slight decrease in EDTA treatment. To complement the AFM measurements, the HF-SAM was used to obtain the V(z) signatures of the onion epidermis. These measurements were focused on documenting the effect of pectinase enzyme treatment. The results indicate a significant change in the V(z) signature curves with time into the enzyme treatment. Thus AFM and HF-SAM open the door to a systematic nondestructive structure and mechanical property study of complex biological cell walls. A unique feature of this approach is that both microscopes allow the biological samples to be examined in their natural fluid (water) environment.

nanoindentation↗

Dark Energy Survey Year 3 results: optimized $w$CDM simulation-based inference with weak lensing map-level hybrid statistics

We present cosmological constraints from the Dark Energy Survey Year 3 (DES Y3) weak lensing data using hierarchical hybrid statistics within a Bayesian simulation-based inference framework that is based on the Gower Street simulations. To maximize the precision of the inference, we have developed a new, information-theory based, data compression of the weak lensing maps to just seven highly informative summary statistics. The hybrid scheme exploits the high information content of the power spectrum, compressing both the power spectrum and neural-based summaries that are designed to extract further information. Our simulation-based approach enables principled forward modelling of all major sources of systematic uncertainty and survey properties into realistic mock observations, including the survey mask, photometric redshift uncertainties, intrinsic galaxy alignments, multiplicative shear calibration bias, source galaxy clustering, non-Gaussian shape noise, and non-linear structure formation. The summary statistics are then used in a Bayesian simulation-based inference pipeline. The inference is validated through coverage tests and checks for robustness against baryonic feedback. Assuming a $w$CDM cosmology, our analysis yields $S_8 = 0.808 \pm 0.017$, $Ω_{\rm m} = 0.325 \pm 0.024$, and $w < -0.766$ (marginalized posterior 68 per cent credible intervals). This rigorous combination of information theory, physics- and neural network-based extreme data compression, and principled Bayesian analysis improves the figure of merit for $(Ω_{\rm m}, S_8, w)$ by 60 per cent over the previous state-of-the-art, and by almost a factor of 3 over two-point analyses of the same data. They are the most precise joint constraints on $(Ω_{\rm m}, S_8, w)$ from weak gravitational lensing data alone of any survey to date. We intend to apply this analysis to the more recent DES Y6 data.

Williamson, J. [University Coll. London]↗

Architecting the Third Dimension of Electrochemical Energy Storage

Three-dimensional (3D) architectural design has emerged as a powerful strategy to push electrochemical energy storage (EES) devices beyond the intrinsic limitations of conventional two-dimensional (2D) electrodes. While planar architectures enable high packing density and mature manufacturing, they suffer from limited ion transport and low active-material loading. In contrast, 3D architectures introduce low-tortuosity networks and high surface area that enhance charge and mass transport while supporting thick, high mass-loading electrodes. However, their practicality remains hindered by challenges in volumetric density, mechanical stability, and large-scale manufacturability. Here, this Perspective examines the key evaluation and design principles that govern 3D device performance. We discuss the fundamental trade-offs between porosity, volumetric density, and mechanical stability that shape 3D design and highlight emerging strategies for integrating materials engineering, structural optimization, device integration, computational modeling, and scalable manufacturing. By aligning structural functionality with manufacturability, 3D architectures can evolve from laboratory prototypes to commercially viable energy storage systems.

25 ENERGY STORAGE↗

Solid-state rigid-rod polymer composite electrolytes with nanocrystalline lithium ion pathways

A critical challenge for next-generation lithium-based batteries lies in development of electrolytes that enable thermal safety along with use of high-energy-density electrodes. We describe molecular ionic composite (MIC) electrolytes based on an aligned liquid crystalline polymer combined with ionic liquids and concentrated Li salt. This high strength (200 MPa) and non-flammable solid electrolyte possesses outstanding Li + conductivity (1 mS cm -1 at 25 °C) and electrochemical stability (5.6 V vs Li|Li + ) while suppressing dendrite growth and exhibiting low interfacial resistance (32 Ω cm 2 ) and overpotentials (≤ 120 mV @ 1 mA cm -2 ) during Li symmetric cell cycling. A heterogeneous salt doping process modifies a locally ordered polymer-ion assembly to incorporate an inter-grain network filled with defective LiFSI & LiBF 4 nanocrystals, strongly enhancing Li + conduction. Furthermore, this modular material fabrication platform shows promise for safe and high-energy-density energy storage and conversion applications, incorporating the fast transport of ceramic-like conductors with the superior flexibility of polymer electrolytes.

25 ENERGY STORAGE↗

Superprotonic conduction through one-dimensional ordered alkali metal ion chains in a lanthanide-organic framework

Although no evident hydrogen-bond network appears, an ultrahigh proton conductivity of 2.91 × 10 −2 S cm −1 at 363 K and 90% RH with an ultralow activation energy of 0.10 eV was observed in an anionic lanthanide-organic framework Na 2 [Eu(SDB) 2 (COO)]·0.375DMF·0.4H 2 O (1); both values approach the records among all reported proton-conducting MOF materials. This suggests that the proton conduction process in 1 is reminiscent of the Grotthuss mechanism, which together reveals an effective proton transportation pathway associated with aligned Na + and their coordinated water.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Tribal Colleges and Universities/American Indian Higher Education Consortium Advanced Manufacturing Technical Assistance Project

The National Nuclear Security Agency (NNSA) created a Minority Serving Institution Partnership Plan (MSIPP) to 1) align investments in a university capacity and workforce development with the NNSA mission to develop the needed skills and talent for NNSA's enduring technical workforce at the laboratories and production plants and 2) to enhance research and education at underrepresented colleges and universities. Out of this effort, MSIPP launched a new program in early FY17 focused on Tribal Colleges and Universities (TCUs). The following report summarizes the project focus and status update during this reporting period. The overall goal of this project is to establish a network of TCUs with essential advanced manufacturing facilities, associated training and education programs, and private sector and federal agency partnerships to both prepare an American Indian advanced manufacturing workforce and create economic and employment opportunities within Tribal communities through design, manufacturing, and marketing of high quality products. Some examples of high quality products involve next generation grid components such as mechanical energy storage, cabling for distribution of energy, and electrochemical energy storage enclosures.

25 ENERGY STORAGE↗

The AI-Base NDA Catalog

The project objective was to create a catalog of NDA capabilities so that as future challenges emerge, potential solutions can be quickly identified. The initial direction was to create a custom database where we would manually add various NDA technologies. During the year, LANL leadership heavily embraced AI tools and established LANL’s Enterprise ChatGPT license. To align with this vision, instead of a database we created a custom GPT to achieve the same capability. We provided a series of references on NDA technologies, the most comprehensive of which is the 2024 “PANDA manual” which is a 700-page textbook. A custom GPT is a semi-isolated version of OpenAI’s GPT model that can be tailored to specific tasks with context documents and instructions. Anyone with a LANL ChatGPT Enterprise account can access the NDA Catalog. Since the GPT exists on the green network, only non-sensitive questions may be asked. The Catalog can be used by simply asking questions in conversational English. We have found the Catalog to be quite accurate, even for heavily technical, complex queries. The Catalog allows the user to describe a measurement problem and it will respond with potential technology solutions.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗

A Strong Gravitational Lens Is Worth a Thousand Dark Matter Halos: Inference on Small-scale Structure Using Sequential Methods

Strong gravitational lenses are a singular probe of the Universe’s small-scale structure—they are sensitive to the gravitational effects of low-mass (<10 10 M ⊙ ) halos even without a luminous counterpart. Recent strong-lensing analyses of dark matter structure rely on simulation-based inference (SBI). Modern SBI methods, which leverage neural networks as density estimators, have shown promise in extracting the halo-population signal. However, it is unclear whether the constraints from these models are limited by the methodology or the data. In this study, we introduce an accelerator-optimized simulation pipeline that can generate lens images with realistic subhalo populations in milliseconds. Leveraging this simulator, we identify the main limitation of our fiducial SBI analysis: training set size. We then adopt a sequential neural posterior estimation (SNPE) approach, allowing us to refine the training distribution to align with the observed data. Using only one-fifth as many mock Hubble Space Telescope images, SNPE matches the constraints on the low-mass halo population produced by our best nonsequential model. Our experiments suggest that an over 3 order-of-magnitude increase in training set size and GPU hours would be required to achieve an equivalent result without sequential methods. While the full potential of the existing lens sample remains to be explored, the notable improvement in constraining power enabled by our sequential approach highlights that current constraints are limited primarily by methodology and not the data itself. Moreover, our results emphasize the need to treat training set generation and model optimization as interconnected stages of any cosmological analysis using SBI.

79 ASTRONOMY AND ASTROPHYSICS↗

Cavitation-based hydro-fracturing technique for geothermal reservoir stimulation

A rotary shutter valve 500 is used for geothermal reservoir stimulation. The valve 500 includes a pressure chamber 520 for holding a working fluid (F) under pressure. A rotatable shutter 532 is turned with a powering device 544 to periodically align one or more windows 534 with one or more apertures 526 in a bulkhead 524. When aligned, the pressurized working fluid (F) flows through the bulkhead 524 and enters a pulse cavity 522, where it is discharged from the pulse cavity 522 as pressure waves 200. The pressure wave propagation 200 and eventual collapse of the bubbles 202 can be transmitted to a target rock surface 204 either in the form of a shock wave 206, or by micro jets 208, depending on the bubble-surface distance. Once cavitation at the rock face begins, fractures are initiated in the rock to create a network of micro-fissures for enhanced heat transfer.

15 GEOTHERMAL ENERGY↗

Electronic and Optical Properties of Two-dimensional Covalent Organic Frameworks

Two-dimensional covalent organic film with macrocyclic network under simple solvothermal conditions was recently synthesized experimentally [Colson, J. W.; et al. Science 2011, 332, 228], which offers immense potentials for optoelectronic applications as in the case of graphene. Here we systematically investigate the electronic and optical properties of such novel covalent organic frameworks (COF-5, TP-COF and NiPc PBBA-COF) as free-standing sheets using density-functional theory. The results shed considerable light on the nature of spatial carrier confinement with band offset. COF-5 exhibits a type-II heterojunction alignment with the significant valence and conduction band offsets, suggesting an effective spatial carrier separation of electrons and holes. In TP-COF, the valence offset is close to zero related to the dispersed distribution of photoexcited holes over the entire structure, while the conduction-band offset is still remarkable, indicating the effective confinement of photoexcited electrons. NiPc PBBACOF presents a type-I heterojunction alignment where the band-edged wave functions are localized in the same region, achieving the effective spatial carrier congregation. The calculated absorption peaks of the optical absorption of TP-COF and NiPc-PBBA COF frameworks are in agreement with experimental measurements, thus providing theoretical insights into experimental observed transmission spectra of these frameworks.

36 MATERIALS SCIENCE↗

Classification of Unintended Radiated Emissions in a Multi-Device Environment

Here, unintended radiated emissions (URE) from electronic devices are conducted on to the power infrastructure and can be collected and analyzed for non-intrusive load monitoring (NILM) applications. Dimensionally aligned signal projections (DASP) were previously introduced as specialized signal transforms to enable device classification using machine learning on statistical features derived from the DASP transforms. In this work, we explore multi-device classification which requires more sophisticated methods owing to the complexity of the feature space such as large dynamic range, non-contiguous features, over-lapping features, and nonlinear interactions of features. In particular, we introduce an additional DASP algorithm for increased resolution and sensitivity to intermodulation products and examine the utility of convolutional neural networks (CNNs) to extract and learn features directly from DASP images for classification of single and multiple device URE captures.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Crystal structures of the naturally fused CS and cytochrome b 5 reductase ( b 5 R) domains of Ncb5or reveal an expanded CS fold, extensive CS– b 5 R interactions and productive binding of the NAD(P) + nicotinamide ring

Ncb5or (NADH-cytochrome b 5 oxidoreductase), a cytosolic ferric reductase implicated in diabetes and neurological diseases, comprises three distinct domains, cytochrome b 5 ( b 5 ) and cytochrome b 5 reductase ( b 5 R) domains separated by a CHORD–Sgt1 (CS) domain, and a novel 50-residue N-terminal region. Understanding how interdomain interactions in Ncb5or facilitate the shuttling of electrons from NAD(P)H to heme, and how the process compares with the microsomal b 5 (Cyb5A) and b 5 R (Cyb5R3) system, is of interest. A high-resolution structure of the b 5 domain (PDB entry 3lf5) has previously been reported, which exhibits substantial differences in comparison to Cyb5A. The structural characterization of a construct comprising the naturally fused CS and b 5 R domains with bound FAD and NAD + (PDB entry 6mv1) or NADP + (PDB entry 6mv2) is now reported. The structures reveal that the linker between the CS and b 5 R cores is more ordered than predicted, with much of it extending the β-sandwich motif of the CS domain. This limits the flexibility between the two domains, which recognize one another via a short β-sheet motif and a network of conserved side-chain hydrogen bonds, salt bridges and cation–Π interactions. Notable differences in FAD–protein interactions in Ncb5or and Cyb5R3 provide insight into the selectivity for docking of their respective b 5 redox partners. The structures also afford a structural explanation for the unusual ability of Ncb5or to utilize both NADH and NADPH, and represent the first examples of native, fully oxidized b 5 R family members in which the nicotinamide ring of NAD(P) + resides in the active site. Finally, the structures, together with sequence alignments, show that the b 5 R domain is more closely related to single-domain Cyb5R proteins from plants, fungi and some protists than to Cyb5R3 from animals.

Benson, David R.↗

Model Cortical Association Fields Account for the Time Course and Dependence on Target Complexity of Human Contour Perception

Can lateral connectivity in the primary visual cortex account for the time dependence and intrinsic task difficulty of human contour detection? To answer this question, we created a synthetic image set that prevents sole reliance on either low-level visual features or high-level context for the detection of target objects. Rendered images consist of smoothly varying, globally aligned contour fragments (amoebas) distributed among groups of randomly rotated fragments (clutter). The time course and accuracy of amoeba detection by humans was measured using a two-alternative forced choice protocol with self-reported confidence and variable image presentation time (20-200 ms), followed by an image mask optimized so as to interrupt visual processing. Measured psychometric functions were well fit by sigmoidal functions with exponential time constants of 30-91 ms, depending on amoeba complexity. Key aspects of the psychophysical experiments were accounted for by a computational network model, in which simulated responses across retinotopic arrays of orientation-selective elements were modulated by cortical association fields, represented as multiplicative kernels computed from the differences in pairwise edge statistics between target and distractor images. Comparing the experimental and the computational results suggests that each iteration of the lateral interactions takes at least 37.5 ms of cortical processing time. Our results provide evidence that cortical association fields between orientation selective elements in early visual areas can account for important temporal and task-dependent aspects of the psychometric curves characterizing human contour perception, with the remaining discrepancies postulated to arise from the influence of higher cortical areas.

59 BASIC BIOLOGICAL SCIENCES↗